Computer Science And Technology - Research Publications
Permanent URI for this collectionhttps://kr.cup.edu.in/handle/32116/82
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Item Advances and challenges in thyroid cancer: The interplay of genetic modulators, targeted therapies, and AI-driven approaches(Elsevier Inc., 2023-09-20T00:00:00) Bhattacharya, Srinjan; Mahato, Rahul Kumar; Singh, Satwinder; Bhatti, Gurjit Kaur; Mastana, Sarabjit Singh; Bhatti, Jasvinder SinghThyroid cancer continues to exhibit a rising incidence globally, predominantly affecting women. Despite stable mortality rates, the unique characteristics of thyroid carcinoma warrant a distinct approach. Differentiated thyroid cancer, comprising most cases, is effectively managed through standard treatments such as thyroidectomy and radioiodine therapy. However, rarer variants, including anaplastic thyroid carcinoma, necessitate specialized interventions, often employing targeted therapies. Although these drugs focus on symptom management, they are not curative. This review delves into the fundamental modulators of thyroid cancers, encompassing genetic, epigenetic, and non-coding RNA factors while exploring their intricate interplay and influence. Epigenetic modifications directly affect the expression of causal genes, while long non-coding RNAs impact the function and expression of micro-RNAs, culminating in tumorigenesis. Additionally, this article provides a concise overview of the advantages and disadvantages associated with pharmacological and non-pharmacological therapeutic interventions in thyroid cancer. Furthermore, with technological advancements, integrating modern software and computing into healthcare and medical practices has become increasingly prevalent. Artificial intelligence and machine learning techniques hold the potential to predict treatment outcomes, analyze data, and develop personalized therapeutic approaches catering to patient specificity. In thyroid cancer, cutting-edge machine learning and deep learning technologies analyze factors such as ultrasonography results for tumor textures and biopsy samples from fine needle aspirations, paving the way for a more accurate and effective therapeutic landscape in the near future. � 2023 The Author(s)Item A systematic review of artificial intelligence in agriculture(Elsevier, 2022-01-15T00:00:00) Singh, Parvinder; Kaur, AmandeepThe current world population is 7.8 billion and is projected to reach 9.8 billion by 2050. The limited land area and strong need to produce more crop to feed the ever-increasing population is a major challenge today, especially for developing countries. The strong need to produce more crop from lesser land has led to several challenges in the field of agriculture. Reduction in agriculture yield due to climate change and global warming due to farming has become a vicious circle. Excessive use of chemicals in farms to increase soil fertility and reduce weeds and pests have adversely affected the environment and the human health. There is limited availability of natural resources like phosphorous and energy required in agriculture. Water scarcity and increase in plant diseases are other major concerns. Artificial intelligence (AI) has emerged as a promising technology in digital agriculture. Digital agriculture relates to using digital technologies for collecting, storing, and further analyzing the electronic agricultural data for better reasoning and decision-making using AI techniques. Precision agriculture is one such technique that monitors soil moisture and composition, temperature, and humidity and determines optimized fertilizer and water requirements for a specific crop and different areas of a farm. Then there are computer vision and machine learning techniques to detect diseases and deficiencies in plants, recognizing weeds that helps in spraying only those parts of land where the plants are disease-infected or where weeds are present instead of the whole field. Utilization of AI in agriculture is helping in developing agricultural methods capable of increasing crop yield and reducing the previously stated challenges. With merits of using AI, there are certain issues. The first major issue in using the AI techniques is the need for high computational power that, again, leads to global warming. Also, in developing countries, the internet infrastructure needs to be improved to use AI techniques effectively. Cost of using AI is high, and countries need AI experts to use the techniques to full potential. The focus of this chapter is to review how AI techniques are helping in increasing yield and overcoming limitations, like global warming, excessive use of fertilizers, limited availability of natural resources, plant disease, and water scarcity. The chapter concludes by discussing the issues and challenges in using AI, especially as it related to agriculture. � 2022 Elsevier Inc. All rights reserved.Item Detection of malicious URLs in big data using RIPPER algorithm(Institute of Electrical and Electronics Engineers Inc., 2018) Thakur, S.; Meenakshi, E.; Priya, A.'Big Data' is the term that describes a large amount of datasets. Datasets like web logs, call records, medical records, military surveillance, photography archives, etc. are often so large and complex, and as the data is stored in Big Data in the form of both structured and unstructured therefore, big data cannot be processed using database queries like SQL queries. In big data, malicious URLs have become a station for internet criminal activities such as drive-by-download, information warfare, spamming and phishing. Malicious URLs detection techniques can be classified into Non-Machine Learning (e.g. blacklisting) and Machine learning approach (e.g. data mining techniques). Data mining helps in the analysis of large and complex datasets in order to detect common patterns or learn new things. Big data is the collection of large and complex datasets and the processing of these datasets can be done either by using tool like Hadoop or data mining algorithms. Data mining techniques can generate classification models which is used to manage data, modelling of data that helps to make prediction about whether it is malicious or legitimate. In this paper analysis of RIPPER i.e. JRip data mining algorithm has been done using WEKA tool. A training dataset of 6000 URLs has been made to train the JRip algorithm which is an implementation of RIPPER algorithm in WEKA. Training dataset will generate a model which is used to predict the testing dataset of 1050 URLs. Accuracy are calculated after testing process. Result shows JRip has an accuracy of 82%. ? 2017 IEEE.Item Comparison of classification techniques for intrusion detection dataset using WEKA(Institute of Electrical and Electronics Engineers Inc., 2014) Garg, T.; Khurana, S.S.As the network based applications are growing rapidly, the network security mechanisms require more attention to improve speed and precision. The ever evolving new intrusion types pose a serious threat to network security. Although numerous network security tools have been developed, yet the fast growth of intrusive activities is still a serious issue. Intrusion detection systems (IDSs) are used to detect intrusive activities on the network. Machine learning and classification algorithms help to design 'Intrusion Detection Models' which can classify the network traffic into intrusive or normal traffic. In this paper we present the comparative performance of NSL-KDD based data set compatible classification algorithms. These classifiers have been evaluated in WEKA (Waikato Environment for Knowledge Analysis) environment using 41 attributes. Around 94,000 instances from complete KDD dataset have been included in the training data set and over 48,000 instances have been included in the testing data set. Garrett's Ranking Technique has been applied to rank different classifiers according to their performance. Rotation Forest classification approach outperformed the rest. ? 2014 IEEE.